대한인간공학회지 (Journal of the Ergonomics Society of Korea)

  • 제25권2호
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  • Pages.161-172
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  • 2006
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  • 1229-1684(pISSN)
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  • 2093-8462(eISSN)
대한인간공학회 (Ergonomics Society of Korea)
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감성모델링 기법 차이에 따른 휴대전화 고급감 모델의 비교 평가

A Comparison of Modeling Methods for a Luxuriousness Model of Mobile Phones

  • 발행 : 2006.05.31
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초록

This study aims to compare and contrast the Kansei modeling methods for building a luxuriousness model that people feel about appearance of mobile phones. For the evaluation based on Kansei engineering approaches, 15 participants were employed to evaluate 18 mobile phones using a questionnaire. The results of evaluation were analyzed to build luxuriousness models through quantification I method, neural network, and decision tree method, respectively. The performance of Kansei modeling methods was compared and contrasted in terms of accuracy and predictability. The result of comparison of modeling methods indicated that model accuracy and predictability was closely related to the number of variables and data size. It was also revealed that quantification I method was the best in terms of model accuracy while decision tree method was the best modeling method with small variance in terms of predictability. However, it was empirically found that quantification I method showed extremely unstable predictability with small number of data. Consequently, it is expected that the research findings of this study might be utilized as a guideline for selecting proper Kansei modeling method.

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참고문헌

  1. 이민훈, 기술과 감성의 융합시대, 삼성경제연구소 CEO Information, 417, 8-9, 2003
  2. 한성호, 제품디자인의 감성만족도 평가 및 예측모델 개발, 대한인간공학회지, 20(1), 87-113, 2001
  3. Breiman, L., Friedman, J. H., Olshen, R. A. and Stone, C. J., Classification and Regression Trees, Wadsworth Statistic/Probability Series, Wadsworth Advanced Books and Software, Belmont, CA, 1984
  4. Brown, D. E., Corruble, V. and Pittard, C. L., A comparison of decision tree classifiers with backpropagation neural networks for multimodal classification problems, Pattern Recognition, 26(6), 953-961, 1993 https://doi.org/10.1016/0031-3203(93)90060-A
  5. Brown-Brandl, T. M., Jones, D. D. and Woldt, W. E., Evaluating Modeling Techniques for Cattle Heat Stress Prediction, Biosystems Engineering, 91(4), 513-524, 2005 https://doi.org/10.1016/j.biosystemseng.2005.04.003
  6. Ekkekakis, P. and Petruzzello, S. J., Analysis of the affect measurement conundrum in exercise psychology: IV. A conceptual case for the affect circumplex, Psychology of Sport and Exercise, 3, 35-63, 2002 https://doi.org/10.1016/S1469-0292(01)00028-0
  7. Fukuda, S. and Matsuura, Y., Prioritizing the customer's requirements by AHP for concurrent design, Design for Manufacturability, 52, 13-19, 1993
  8. Fukushima, K., Kawata, H., Fujiwara, Y. and Genno, H., Human sensory perception oriented image processing in color copy system, International Journal of Industrial Ergonomics, 15(1), 63-74, 1995 https://doi.org/10.1016/0169-8141(94)00057-A
  9. Green, P. E. and DeSarbo, W. S., Additive Decomposition of Perceptions Data Via Conjoint Analysis, Journal of Consumer Research, 5, 58-65, 1987 https://doi.org/10.1086/208714
  10. Han, S. H., Yun, M. H., Kim, K. J. and Kwahk, J. Y., Evaluation of product usability: development and validation of usability dimensions and design elements based on empirical models, International Journal of Industrial Ergonomics, 26, 477-488, 2000 https://doi.org/10.1016/S0169-8141(00)00019-6
  11. Han, S. H. and Hong, S. W., A systematic approach for coupling user satisfaction with product design, Ergonomics, 46(13), 1441-1461, 2003 https://doi.org/10.1080/00140130310001610928
  12. Hosmer, D. W. and Lemeshow, S., Applied Logistic Regression, New York, Wiley, 1989
  13. Hruschka, H., Determining market response functions by neural network modeling: A comparison to econometric techniques, European Journal of Operational Research, 66, 27-35, 1993 https://doi.org/10.1016/0377-2217(93)90203-Y
  14. Ishihara, S., Ishihara, K., Nagamachi, M. and Matsubara, Y., An automatic builder for a Kansei Engineering expert system using self-organizing neural networks, International Journal of Industrial Ergonomics, 15(1), 13-24, 1995 https://doi.org/10.1016/0169-8141(94)15053-8
  15. Jindo, T. and Nagamachi, M., The development of a car interior image system incorporating knowledge engineering and computer graphics, In: Queinnec, Y. and Daniellou, F. (Eds.), Proceedings of the 11th Congress of the International Ergonomics Association, 625-627, 1991
  16. Jindo, T., Hirasago, K. and Nagamachi, M., Development of a design-support system for office chairs using 3-D graphics, International Journal of Industrial Ergonomics, 15(1), 49-62, 1995 https://doi.org/10.1016/0169-8141(94)00056-9
  17. Jindo, T. and Hirasago, K., Application studies to car interior of Kansei engineering, International Journal of Industrial Ergonomics, 19(2), 105-114, 1997 https://doi.org/10.1016/S0169-8141(96)00007-8
  18. Kano, N., Seraku, N., Takahashi, F. and Tsuji, S., Attractive quality and must be quality, Hinshitsu, The Journal of the Japanese Society for Quality Control, 14(2), 39-48, 1984
  19. Kolich, M., Predicting automobile seat comfort using a neural network, International Journal of Industrial Ergonomics, 33(4), 285-293, 2004 https://doi.org/10.1016/j.ergon.2003.10.004
  20. Laros, F. J. M. and Steenkamp, J.-B.E.M., Emotions in consumer behavior: a hierarchical approach, Journal of Business Research, 58, 1437-1445, 2005 https://doi.org/10.1016/j.jbusres.2003.09.013
  21. Liu, Y., Engineering aesthetics and aesthetic ergonomics: Theoretical foundations and a dual-process research methodology, Ergonomics, 46(13/14), 1273-1292, 2003 https://doi.org/10.1080/00140130310001610829
  22. Louviere, J. and Timmermans, H., Stated preference and choice models applied to recreation research: a review, Leisure Sciences, 12, 9-32, 1990 https://doi.org/10.1080/01490409009513087
  23. McAdams, D. A., Stone, R. B. and Wood, K. L., Functional Interdependence and Product Similarity Based on Customer Needs, Research in Engineering Design, 11, 1-19, 1999 https://doi.org/10.1007/s001630050001
  24. Markham, I. S., Mathieu, R. G. and Wray, B. A., Kanban setting through artificial intelligence: A comparative study of artificial neural networks and decision trees, Integrated Manufacturing, 11(4), 239, 2000 https://doi.org/10.1108/09576060010326230
  25. Nagamachi, M., Image technology based on knowledge engineering and its application for design consultation, In: Adams, A.S., Hall, R.R., McPhee, B. J., Oxenburgh, M. S. (Eds.), Proceedings of the 10th Congress of the International Ergonomics Association, 72-74, 1988
  26. Nagamachi, M., An image technology expert system and its application to design consultation, International Journal of Human-Computer Interaction, 3(3), 267-279, 1991 https://doi.org/10.1080/10447319109526012
  27. Nagamachi, M., Kansei Engineering: A new ergonomic consumer-oriented technology for product development, International Journal of Industrial Ergonomics, 15, 3-11, 1995 https://doi.org/10.1016/0169-8141(94)00052-5
  28. Nagamachi, M., Kansei engineering and implementation of human-oriented product design, In: Koubek, R.J., Karwowski, W. (Eds.), Manufacturing Agility and Hybrid Automation-I, IEA Press, Louisville, KY, 77-80, 1996
  29. Nakata, K., Iwata, T. and Nagamachi, M., Color and Kansei on miniconstruction vehicle, Human Interface, 9(1), 23-26, 1994
  30. Perner, P., Zscherpel, U. and Jacobsen, C., A comparison between neural networks and decision trees based on data from industrial radiographic testing, Pattern Recognition Letters, 22, 47-54, 2001 https://doi.org/10.1016/S0167-8655(00)00098-2
  31. Petersen, D., Teamwork: New Management Ideas for the Nineties, Victor Gollancz, London. 1992
  32. Razi, M. A. and Athappilly, K., A comparative predictive analysis of Neural Networks (NN's), Nonlinear Regression and Classification and Regression Tree (CART) models, Expert Systems with Applications, 29(1), 65-74, 2005 https://doi.org/10.1016/j.eswa.2005.01.006
  33. Russell, J. A., A circumplex model of affect, Journal of Personality and Social Psychology, 39, 1161-1178, 1980 https://doi.org/10.1037/h0077714
  34. Russell, J. A. and Steiger, J. H., The structure in person's implicit taxanomy of emotions, Journal of Research in Personality, 16, 447-469, 1982 https://doi.org/10.1016/0092-6566(82)90005-8
  35. Sethuraman, R., Kerin, R. A. and Cron, W. L., A field study comparing online and offline data collection methods for identifying product attribute preferences using conjoint analysis, Journal of Business Research, 58, 602-610, 2005 https://doi.org/10.1016/j.jbusres.2003.09.009
  36. Sharda, R. and Delen, D., Predicting box-office success of motion pictures with neural networks, Expert Systems with Applications, 243-254, 2003 https://doi.org/10.1016/j.eswa.2005.07.018
  37. Shimizu, Y. and Jindo, T., A fuzzy logic analysis method for evaluation human sensitivities, International Journal of Industrial Ergonomics, 15, 39-47, 1995 https://doi.org/10.1016/0169-8141(95)91249-A
  38. Taylor, J. G., Neural networks and their applications, Wiley, England, 1996
  39. Turksen, I. B. and Wilson, I.A. Consumer preference models: fuzzy theory approach, Proceeding of SPIE, 2061, 203-211, 1993
  40. Yun, M. H., Han, S. H., Hong, S. W. and Kim, J. S., Incorporating user satisfaction into the look-and-feel of mobile phone design, Ergonomics, 46(13/14), 1423-1440, 2003 https://doi.org/10.1080/00140130310001610919
  41. Zhang, W., Cao, Q. and Schniederjans, M. J., Neural networks earning per share forecasting models: A comparative analysis of alternative methods, Decision Sciences, 35(2), 205-237, 2004 https://doi.org/10.1111/j.00117315.2004.02674.x

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